AI-Genetic-Pro
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AND MODIFICATION
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lib/AI/Genetic/Pro.pm view on Meta::CPAN
}
#=======================================================================
sub _init_cache {
my ($self) = @_;
$self->_fitness_real($self->fitness);
$self->fitness(\&_fitness_cached);
return;
}
#=======================================================================
sub _check_data_ref {
my ($self, $data_org) = @_;
my $data = clone($data_org);
my $ars;
for(0..$#$data){
next if $ars->{$data->[$_]};
$ars->{$data->[$_]} = 1;
unshift @{$data->[$_]}, undef;
}
return $data;
}
#=======================================================================
# we have to find C to (in some cases) incrase value of range
# due to design model
sub _find_fix_range {
my ($self, $data) = @_;
for my $idx (0..$#$data){
if($data->[$idx]->[1] < 1){
my $const = 1 - $data->[$idx]->[1];
push @{$self->_fix_range}, $const;
$data->[$idx]->[1] += $const;
$data->[$idx]->[2] += $const;
}else{ push @{$self->_fix_range}, 0; }
}
return $data;
}
#=======================================================================
sub init {
my ( $self, $data ) = @_;
croak q/You have to pass some data to "init"!/ unless $data;
#-------------------------------------------------------------------
$self->generation(0);
$self->_init( $data );
$self->_fitness( { } );
$self->_fix_range( [ ] );
$self->_history( [ [ ], [ ], [ ] ] );
$self->_init_cache if $self->cache;
#-------------------------------------------------------------------
if($self->type eq q/listvector/){
croak(q/You have to pass array reference if "type" is set to "listvector"/) unless ref $data eq 'ARRAY';
$self->_translations( $self->_check_data_ref($data) );
}elsif($self->type eq q/bitvector/){
croak(q/You have to pass integer if "type" is set to "bitvector"/) if $data !~ /^\d+$/o;
$self->_translations( [ [ 0, 1 ] ] );
$self->_translations->[$_] = $self->_translations->[0] for 1..$data-1;
}elsif($self->type eq q/combination/){
croak(q/You have to pass array reference if "type" is set to "combination"/) unless ref $data eq 'ARRAY';
$self->_translations( [ clone($data) ] );
$self->_translations->[$_] = $self->_translations->[0] for 1..$#$data;
}elsif($self->type eq q/rangevector/){
croak(q/You have to pass array reference if "type" is set to "rangevector"/) unless ref $data eq 'ARRAY';
$self->_translations( $self->_find_fix_range( $self->_check_data_ref($data) ));
}else{
croak(q/You have to specify first "type" of vector!/);
}
my $size = 0;
if($self->type ne q/rangevector/){ for(@{$self->_translations}){ $size = $#$_ if $#$_ > $size; } }
# else{ for(@{$self->_translations}){ $size = $_->[1] if $_->[1] > $size; } }
else{ for(@{$self->_translations}){ $size = $_->[2] if $_->[2] > $size; } } # Provisional patch for rangevector values truncated to signed 8-bit quantities. Thx to Tod Hagan
my $package = get_package_by_element_size($size);
$self->_package($package);
my $length = ref $data ? sub { $#$data; } : sub { $data - 1 };
if($self->variable_length){
$length = ref $data ? sub { 1 + int( rand( $#{ $self->_init } ) ); } : sub { 1 + int( rand( $self->_init - 1) ); };
}
$self->_length( $length );
$self->chromosomes( [ ] );
push @{$self->chromosomes},
AI::Genetic::Pro::Chromosome->new($self->_translations, $self->type, $package, $length->())
for 1..$self->population;
$self->_calculate_fitness_all();
lib/AI/Genetic/Pro.pm view on Meta::CPAN
}
#=======================================================================
# CHARTS ###############################################################
#=======================================================================
sub chart {
GD->require or croak(q/You need "/.GD.q/" module to draw chart of evolution!/);
my ($self, %params) = (shift, @_);
my $graph = GD()->new(($params{-width} || 640), ($params{-height} || 480));
my $data = $self->getHistory;
if(defined $params{-font}){
$graph->set_title_font ($params{-font}, 12);
$graph->set_x_label_font($params{-font}, 10);
$graph->set_y_label_font($params{-font}, 10);
$graph->set_legend_font ($params{-font}, 8);
}
$graph->set_legend(
$params{legend1} || q/Max value/,
$params{legend2} || q/Mean value/,
$params{legend3} || q/Min value/,
);
$graph->set(
x_label_skip => int(($data->[0]->[-1]*4)/100),
x_labels_vertical => 1,
x_label_position => .5,
y_label_position => .5,
y_long_ticks => 1, # poziome linie
x_ticks => 1, # poziome linie
l_margin => 10,
b_margin => 10,
r_margin => 10,
t_margin => 10,
lib/AI/Genetic/Pro.pm view on Meta::CPAN
transparent => 0,
title => ($params{'-title'} || q/Evolution/ ),
x_label => ($params{'-x_label'} || q/Generation/),
y_label => ($params{'-y_label'} || q/Value/ ),
( $params{-logo} && -f $params{-logo} ? ( logo => $params{-logo} ) : ( ) )
);
my $gd = $graph->plot( [ [ 0..$#{$data->[0]} ], @$data ] ) or croak($@);
open(my $fh, '>', $params{-filename}) or croak($@);
binmode $fh;
print $fh $gd->png;
close $fh;
return 1;
}
#=======================================================================
# TRANSLATIONS #########################################################
#=======================================================================
lib/AI/Genetic/Pro.pm view on Meta::CPAN
push @{$_[0]->_history->[1]}, $tmp[1];
push @{$_[0]->_history->[2]}, $tmp[2];
return 1;
}
#=======================================================================
sub inject {
my ($self, $candidates) = @_;
for(@$candidates){
push @{$self->chromosomes},
AI::Genetic::Pro::Chromosome->new_from_data($self->_translations, $self->type, $self->_package, $_, $self->_fix_range);
$self->_fitness->{$#{$self->chromosomes}} = $self->fitness()->($self, $self->chromosomes->[-1]);
}
$self->_strict( [ ] );
$self->population( $self->population + scalar( @$candidates ) );
return 1;
}
#=======================================================================
sub _state {
lib/AI/Genetic/Pro/Chromosome.pm view on Meta::CPAN
package AI::Genetic::Pro::Chromosome;
$AI::Genetic::Pro::Chromosome::VERSION = '1.009';
use warnings;
use strict;
use List::Util qw(shuffle first);
use List::MoreUtils qw(first_index);
use Tie::Array::Packed;
#use Math::Random qw(random_uniform_integer);
#=======================================================================
sub new {
my ($class, $data, $type, $package, $length) = @_;
my @genes;
tie @genes, $package if $package;
if($type eq q/bitvector/){
#@genes = random_uniform_integer(scalar @$data, 0, 1); # this is fastest, but uses more memory
@genes = map { rand > 0.5 ? 1 : 0 } 0..$length; # this is faster
#@genes = split(q//, unpack("b*", rand 99999), $#$data + 1); # slow
}elsif($type eq q/combination/){
#@genes = shuffle 0..$#{$data->[0]};
@genes = shuffle 0..$length;
}elsif($type eq q/rangevector/){
@genes = map { $_->[1] + int rand($_->[2] - $_->[1] + 1) } @$data[0..$length];
}else{
@genes = map { 1 + int(rand( $#{ $data->[$_] })) } 0..$length;
}
return bless \@genes, $class;
}
#=======================================================================
sub new_from_data {
my ($class, $data, $type, $package, $values, $fix_range) = @_;
die qq/\nToo many elements in the injected chromosome of type "$type": @$values\n/ if $#$values > $#$data;
my @genes;
tie @genes, $package if $package;
if($type eq q/bitvector/){
die qq/\nInproper value in the injected chromosome of type "$type": @$values\n/
if first { not defined $_ or ($_ != 0 and $_ != 1) } @$values;
@genes = @$values;
}elsif($type eq q/combination/){
die qq/\nToo few elements in the injected chromosome of type "$type": @$values\n/
if $#$values != $#{$data->[0]};
for my $idx(0..$#$values){
my $id = first_index { $_ eq $values->[$idx] } @{$data->[0]}; # pomijamy poczatkowy undef
die qq/\nInproper element in the injected chromosome of type "$type": @$values\n/ if $id == -1;
push @genes, $id;
}
}elsif($type eq q/rangevector/){
for my $idx(0..$#$values){
if(defined $values->[$idx]){
my $min = $data->[$idx]->[1] - $fix_range->[$idx];
my $max = $data->[$idx]->[2] - $fix_range->[$idx];
die qq/\nValue out of scope in the injected chromosome of type "$type": @$values\n/
if $values->[$idx] > $max or $values->[$idx] < $min;
push @genes, $values->[$idx] + $fix_range->[$idx];
}else{ push @genes, 0; }
}
}else{
for my $idx(0..$#$values){
my $id = first_index {
not defined $values->[$idx] and not defined $_ or
defined $_ and defined $values->[$idx] and $_ eq $values->[$idx]
} @{$data->[$idx]}; # pomijamy poczatkowy undef
die qq/\nInproper element in the injected chromosome of type "$type": @$values\n/ if $id == -1;
push @genes, $id;
}
}
return bless \@genes, $class;
}
#=======================================================================
sub clone
{
t/01_inject.t view on Meta::CPAN
);
# init population of 32-bit vectors
$ga->init(BITS);
my $population = [ ];
for my $chromosome(@{$ga->chromosomes}){
push @$population, $chromosome->clone;
}
my @data;
for(0..BITS){
my @chromosome;
push @chromosome, rand() < 0.5 ? 1 : 0 for 1..BITS;
push @data, \@chromosome;
}
push @$population, @data;
$ga->inject(\@data);
my $OK = 1;
for(0..$#$population){
my @tmp0 = @{$population->[$_]};
my @tmp1 = @{$ga->chromosomes->[$_]};
unless(compare(\@tmp0, \@tmp1)){
$OK = 0;
last;
}
}
t/06_listvectors_constant_length.t view on Meta::CPAN
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
t/07_listvectors_variable_length_I.t view on Meta::CPAN
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
);
my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
t/08_listvectors_variable_length_II.t view on Meta::CPAN
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
t/09_rangevectors_constant_length.t view on Meta::CPAN
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
t/10_rangevectors_variable_length_I.t view on Meta::CPAN
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
t/11_rangevectors_variable_length_II.t view on Meta::CPAN
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
t/12_combinations_constant_length.t view on Meta::CPAN
-strategy => [ 'PMX' ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
$ga->init( [ 'a'..'h' ] );
my @data = (
[qw( a c b d e g f h )],
[qw( a b d c e f h g )],
[qw( a c b d f e g h )],
[qw( h b c d e f g a )],
);
push @data, @data for 1..scalar(@Win);
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));
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